Understanding the evolution of online news communities is essential for designing more effective news recommender systems. However, due to the lack of appropriate datasets and platforms, the existing literature is limited in understanding the impact of recommender systems on this evolutionary process and the underlying mechanisms, resulting in sub-optimal system designs that may affect long-term utilities. In this work, we propose SimuLine, a simulation platform to dissect the evolution of news recommendation ecosystems and present a detailed analysis of the evolutionary process and underlying mechanisms. SimuLine first constructs a latent space well reflecting the human behaviors, and then simulates the news recommendation ecosystem via agent-based modeling. Based on extensive simulation experiments and the comprehensive analysis framework consisting of quantitative metrics, visualization, and textual explanations, we analyze the characteristics of each evolutionary phase from the perspective of life-cycle theory, and propose a relationship graph illustrating the key factors and affecting mechanisms. Furthermore, we explore the impacts of recommender system designing strategies, including the utilization of cold-start news, breaking news, and promotion, on the evolutionary process, which shed new light on the design of recommender systems.

翻译：理解在线新闻社区的演化对于设计更高效的新闻推荐系统至关重要。然而，由于缺乏合适的数据集和平台，现有文献在理解推荐系统对这一演化过程及其内在机制的影响方面存在局限，导致系统设计可能无法实现长期效益的最大化。本文提出SimuLine模拟平台，用于剖析新闻推荐生态系统的演化，并详细分析其演化过程与内在机制。SimuLine首先构建一个能够充分反映人类行为的潜在空间，进而通过基于智能体的建模模拟新闻推荐生态系统。借助大量仿真实验以及涵盖量化指标、可视化与文本解释的综合分析框架，我们从生命周期理论视角解析各演化阶段的特点，并构建关键因素与影响机制的关系图。此外，我们探索了推荐系统设计策略（包括冷启动新闻、突发新闻与推广机制的运用）对演化过程的影响，为推荐系统的设计提供了新见解。